diff --git a/plots/heatmap-basic/implementations/altair.py b/plots/heatmap-basic/implementations/altair.py
index bfe8f81b90..9863d58aae 100644
--- a/plots/heatmap-basic/implementations/altair.py
+++ b/plots/heatmap-basic/implementations/altair.py
@@ -1,7 +1,7 @@
 """ pyplots.ai
 heatmap-basic: Basic Heatmap
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: altair 6.0.0 | Python 3.14.3
+Quality: 88/100 | Updated: 2026-02-16
 """
 
 import altair as alt
@@ -9,58 +9,133 @@
 import pandas as pd
 
 
-# Data - create a matrix with patterns
+# Data - correlation matrix with realistic weather variables
 np.random.seed(42)
-rows = ["Row A", "Row B", "Row C", "Row D", "Row E", "Row F", "Row G", "Row H"]
-cols = ["Col 1", "Col 2", "Col 3", "Col 4", "Col 5", "Col 6", "Col 7", "Col 8"]
+variables = [
+    "Temperature",
+    "Humidity",
+    "Wind Speed",
+    "Pressure",
+    "Visibility",
+    "Cloud Cover",
+    "Precipitation",
+    "UV Index",
+]
 
-# Generate values with some patterns (diagonal pattern + noise)
-values = []
-for i, row in enumerate(rows):
-    for j, col in enumerate(cols):
-        # Create pattern: higher values near diagonal, add noise
-        base = 100 - abs(i - j) * 12
-        noise = np.random.randn() * 10
-        values.append({"x": col, "y": row, "value": base + noise})
+n_samples = 200
+raw = np.random.randn(n_samples, len(variables))
 
-df = pd.DataFrame(values)
+# Inject realistic correlations with stronger relationships
+raw[:, 1] += raw[:, 0] * 0.6  # Humidity ~ Temperature
+raw[:, 5] += raw[:, 1] * 0.7  # Cloud Cover ~ Humidity
+raw[:, 6] += raw[:, 5] * 0.65  # Precipitation ~ Cloud Cover
+raw[:, 4] -= raw[:, 5] * 0.9  # Visibility inversely ~ Cloud Cover
+raw[:, 7] -= raw[:, 5] * 0.7  # UV Index inversely ~ Cloud Cover
+raw[:, 7] += raw[:, 0] * 0.5  # UV Index ~ Temperature
+raw[:, 3] -= raw[:, 0] * 0.4  # Pressure inversely ~ Temperature
+raw[:, 2] += raw[:, 3] * 0.3  # Wind Speed ~ Pressure
 
-# Plot - heatmap base
+corr = np.corrcoef(raw.T)
+
+# Build long-form dataframe using list comprehension
+df = pd.DataFrame(
+    [
+        {"Row": row_var, "Column": col_var, "value": round(corr[i, j], 2)}
+        for i, row_var in enumerate(variables)
+        for j, col_var in enumerate(variables)
+    ]
+)
+
+# Axis ordering
+axis_order = list(variables)
+
+# Plot - heatmap with colorblind-safe diverging color centered at 0
 heatmap = (
     alt.Chart(df)
-    .mark_rect()
+    .mark_rect(stroke="#ffffff", strokeWidth=1.5, cornerRadius=2)
     .encode(
-        x=alt.X("x:N", title="Column", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
-        y=alt.Y("y:N", title="Row", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+        x=alt.X(
+            "Column:N",
+            title="Weather Variable",
+            sort=axis_order,
+            axis=alt.Axis(
+                labelFontSize=15, titleFontSize=18, labelAngle=-45, orient="top", labelPadding=8, titlePadding=10
+            ),
+        ),
+        y=alt.Y(
+            "Row:N",
+            title="Weather Variable",
+            sort=axis_order,
+            axis=alt.Axis(labelFontSize=15, titleFontSize=18, labelPadding=8, titlePadding=10),
+        ),
         color=alt.Color(
             "value:Q",
-            scale=alt.Scale(scheme="blueorange", domainMid=50),
-            legend=alt.Legend(title="Value", titleFontSize=18, labelFontSize=16),
+            scale=alt.Scale(scheme="blueorange", domain=[-1, 1], domainMid=0),
+            legend=alt.Legend(
+                title="Correlation",
+                titleFontSize=16,
+                labelFontSize=14,
+                gradientLength=300,
+                gradientThickness=16,
+                titlePadding=6,
+                offset=8,
+                direction="vertical",
+            ),
         ),
-        tooltip=["x:N", "y:N", "value:Q"],
+        tooltip=[
+            alt.Tooltip("Column:N", title="Column"),
+            alt.Tooltip("Row:N", title="Row"),
+            alt.Tooltip("value:Q", title="Correlation", format=".2f"),
+        ],
     )
 )
 
-# Add text annotations
+# Highlight cells with strong correlations using thicker borders
+highlight = (
+    alt.Chart(df)
+    .transform_filter((alt.datum.value >= 0.7) | (alt.datum.value <= -0.7))
+    .mark_rect(stroke="#2a2a2a", strokeWidth=2.5, filled=False, cornerRadius=2)
+    .encode(x=alt.X("Column:N", sort=axis_order), y=alt.Y("Row:N", sort=axis_order))
+)
+
+# Text annotations with adaptive color
 text = (
     alt.Chart(df)
-    .mark_text(fontSize=18)
+    .mark_text(fontSize=15, fontWeight="bold")
     .encode(
-        x=alt.X("x:N"),
-        y=alt.Y("y:N"),
-        text=alt.Text("value:Q", format=".0f"),
-        color=alt.condition(alt.datum.value > 70, alt.value("white"), alt.value("black")),
+        x=alt.X("Column:N", sort=axis_order),
+        y=alt.Y("Row:N", sort=axis_order),
+        text=alt.Text("value:Q", format=".2f"),
+        color=alt.when((alt.datum.value > 0.55) | (alt.datum.value < -0.55))
+        .then(alt.value("#ffffff"))
+        .otherwise(alt.value("#333333")),
     )
 )
 
-# Combine heatmap and text, then apply configuration
+# Combine layers and configure
 chart = (
-    (heatmap + text)
-    .properties(width=1400, height=800, title=alt.Title("heatmap-basic · altair · pyplots.ai", fontSize=28))
-    .configure_axis(labelFontSize=16, titleFontSize=20, grid=False)
+    (heatmap + highlight + text)
+    .properties(
+        width=740,
+        height=766,
+        title=alt.Title(
+            "heatmap-basic · altair · pyplots.ai",
+            subtitle=[
+                "Pairwise Pearson correlation coefficients for 8 weather metrics.",
+                "Bold borders highlight strong relationships (|r| ≥ 0.7).",
+            ],
+            fontSize=26,
+            subtitleFontSize=16,
+            subtitleColor="#666666",
+            anchor="start",
+            offset=16,
+        ),
+        padding={"left": 20, "right": 20, "top": 20, "bottom": 20},
+    )
+    .configure_axis(grid=False)
     .configure_view(strokeWidth=0)
 )
 
-# Save
-chart.save("plot.png", scale_factor=3.0)
+# Save — target: 3600×3600 square format
+chart.save("plot.png", scale_factor=3.6)
 chart.save("plot.html")
diff --git a/plots/heatmap-basic/metadata/altair.yaml b/plots/heatmap-basic/metadata/altair.yaml
index e518abf054..31bab66fc1 100644
--- a/plots/heatmap-basic/metadata/altair.yaml
+++ b/plots/heatmap-basic/metadata/altair.yaml
@@ -1,165 +1,187 @@
 library: altair
 specification_id: heatmap-basic
 created: '2025-12-23T00:48:16Z'
-updated: '2025-12-23T01:21:13Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-16T20:39:19Z'
+generated_by: claude-opus-4-6
 workflow_run: 20447968624
 issue: 0
-python_version: 3.13.11
+python_version: 3.14.3
 library_version: 6.0.0
 preview_url: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/altair/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/altair/plot_thumb.png
 preview_html: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/altair/plot.html
-quality_score: 91
+quality_score: 88
 impl_tags:
   dependencies: []
   techniques:
-  - colorbar
   - annotations
-  - html-export
   - layer-composition
+  - hover-tooltips
+  - html-export
   patterns:
   - data-generation
-  dataprep: []
-  styling: []
+  dataprep:
+  - correlation-matrix
+  styling:
+  - custom-colormap
+  - edge-highlighting
 review:
   strengths:
-  - Excellent use of diverging colormap (blueorange) with domainMid=50 to center the
-    color scale
-  - Smart conditional text coloring (white on dark cells, black on light cells) for
-    optimal readability
-  - Clean declarative Altair syntax combining heatmap + text layers
-  - Proper use of tooltips for interactivity in HTML export
-  - Well-proportioned 8x8 matrix with clear diagonal pattern demonstrating heatmap
-    utility
+  - Excellent use of Altair's declarative layer composition (heatmap + highlight borders
+    + text annotations as separate layers)
+  - Adaptive text color using alt.when() conditional encoding ensures readability
+    on both dark and light cells
+  - Bold border highlighting for strong correlations (|r| >= 0.7) adds effective visual
+    storytelling
+  - Realistic weather correlation data with meaningful relationships injected via
+    controlled random correlations
+  - Clean, well-structured code with appropriate complexity for the visualization
+  - Informative subtitle that explains both the data and the highlighting convention
   weaknesses:
-  - Data could include negative values to fully showcase the diverging colormap potential
-  - Generic row/column labels (Row A, Col 1) could be more realistic (e.g., correlation
-    matrix with variable names)
-  - X-axis labels appear slightly rotated which is unnecessary for short labels
-  image_description: The plot displays an 8x8 heatmap with rows labeled "Row A" through
-    "Row H" and columns labeled "Col 1" through "Col 8". The colormap uses a blue-orange
-    diverging scheme (blueorange) with blue representing low values (~8-25) and orange/brown
-    representing high values (~100-110). Each cell displays its numeric value as text
-    annotation - white text on dark cells and black text on light cells. A clear diagonal
-    pattern is visible with higher values along the main diagonal descending from
-    top-left to bottom-right. The title "heatmap-basic · altair · pyplots.ai" appears
-    at the top. A vertical colorbar legend labeled "Value" shows the scale from approximately
-    20 to 100.
+  - Layout balance could be improved — some whitespace imbalance around the colorbar
+    area
+  - Both axes share the identical title 'Weather Variable' which is redundant; consider
+    differentiating or removing one
+  - Feature coverage could include a stronger negative correlation (below -0.8) to
+    better demonstrate the full diverging scale
+  image_description: The plot displays an 8×8 correlation matrix heatmap of weather
+    variables (Temperature, Humidity, Wind Speed, Pressure, Visibility, Cloud Cover,
+    Precipitation, UV Index). It uses a blue-orange diverging color scheme centered
+    at 0, with correlation values annotated in each cell using bold text. The title
+    reads "heatmap-basic · altair · pyplots.ai" with two descriptive subtitle lines
+    explaining the data and the bold-border highlighting. Column labels are rotated
+    -45° at the top axis, row labels on the left. A vertical colorbar on the right
+    displays the correlation scale from -1.0 to 1.0 labeled "Correlation". Strong
+    correlations (|r| ≥ 0.7) are highlighted with darker border outlines. Text color
+    adapts automatically — white text on dark-colored cells, dark text on light-colored
+    cells. The diagonal shows perfect self-correlation (1.00). Notable relationships
+    include Visibility vs Cloud Cover at -0.77 (negative), Cloud Cover vs Precipitation
+    at 0.63 (positive), and Humidity vs Cloud Cover at 0.54 (positive). The layout
+    uses a square format with cells separated by thin white borders with rounded corners.
   criteria_checklist:
     visual_quality:
-      score: 36
-      max: 40
+      score: 27
+      max: 30
       items:
       - id: VQ-01
         name: Text Legibility
-        score: 10
-        max: 10
+        score: 8
+        max: 8
         passed: true
-        comment: Title at 28pt, axis labels at 16-20pt, annotations at 18pt - all
-          perfectly readable
+        comment: 'All font sizes explicitly set: title 26pt, subtitle 16pt, axis labels
+          15pt, axis titles 18pt, annotations 15pt bold'
       - id: VQ-02
         name: No Overlap
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: No overlapping text anywhere
+        comment: No overlapping text; labels rotated -45° prevent collision; annotations
+          fit within cells
       - id: VQ-03
         name: Element Visibility
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: Cells well-sized, clear color differentiation
+        comment: Cells well-sized for 8x8 matrix; white borders separate cells cleanly
       - id: VQ-04
         name: Color Accessibility
         score: 4
-        max: 5
+        max: 4
         passed: true
-        comment: Blue-orange diverging scheme is colorblind-safe, good choice
+        comment: Blue-orange diverging scheme is colorblind-safe
       - id: VQ-05
         name: Layout Balance
-        score: 4
-        max: 5
-        passed: true
-        comment: Good proportions, slight whitespace around edges
+        score: 2
+        max: 4
+        passed: false
+        comment: Square format appropriate but some whitespace imbalance with colorbar
+          placement
       - id: VQ-06
-        name: Axis Labels
+        name: Axis Labels & Title
         score: 1
         max: 2
+        passed: false
+        comment: Weather Variable is descriptive but identical for both axes; no units
+          (not applicable for correlation)
+    design_excellence:
+      score: 14
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
         passed: true
-        comment: '"Column" and "Row" are descriptive but generic (no units expected
-          for this type)'
-      - id: VQ-07
-        name: Grid & Legend
-        score: 1
-        max: 2
+        comment: 'Strong design: adaptive text colors, rounded corners, white cell
+          borders, bold-border highlighting, informative subtitle'
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
         passed: true
-        comment: Colorbar present and well-placed, no grid needed for heatmap (cells
-          serve as grid)
+        comment: Grid removed, view stroke removed, subtle cell borders, generous
+          padding, corner radius styling
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Bold borders guide viewer to strong correlations; subtitle explains
+          what to look for; color highlights extremes
     spec_compliance:
-      score: 25
-      max: 25
+      score: 14
+      max: 15
       items:
       - id: SC-01
         name: Plot Type
-        score: 8
-        max: 8
-        passed: true
-        comment: Correct heatmap using mark_rect()
-      - id: SC-02
-        name: Data Mapping
         score: 5
         max: 5
         passed: true
-        comment: X/Y correctly assigned to columns/rows, value to color
-      - id: SC-03
+        comment: Correct heatmap using mark_rect()
+      - id: SC-02
         name: Required Features
-        score: 5
-        max: 5
+        score: 4
+        max: 4
         passed: true
-        comment: Matrix format, color intensity, diverging colormap with domainMid,
-          value annotations, colorbar legend all present
-      - id: SC-04
-        name: Data Range
+        comment: Diverging colormap, cell annotations, colorbar, logical ordering,
+          strong-correlation highlighting
+      - id: SC-03
+        name: Data Mapping
         score: 3
         max: 3
         passed: true
-        comment: All values visible within scale
-      - id: SC-05
-        name: Legend Accuracy
-        score: 2
-        max: 2
-        passed: true
-        comment: Legend title "Value" is accurate
-      - id: SC-06
-        name: Title Format
+        comment: X/Y correctly mapped as Column and Row weather variables
+      - id: SC-04
+        name: Title & Legend
         score: 2
-        max: 2
-        passed: true
-        comment: Uses correct format "heatmap-basic · altair · pyplots.ai"
+        max: 3
+        passed: false
+        comment: Title format correct; colorbar legend appropriate; both axes share
+          same redundant title
     data_quality:
-      score: 17
-      max: 20
+      score: 14
+      max: 15
       items:
       - id: DQ-01
         name: Feature Coverage
-        score: 6
-        max: 8
+        score: 5
+        max: 6
         passed: true
-        comment: Shows diagonal pattern and value variation well, but lacks negative
-          values to fully demonstrate diverging colormap
+        comment: Shows positive, negative, near-zero correlations and diagonal; good
+          range of strengths
       - id: DQ-02
         name: Realistic Context
-        score: 6
-        max: 7
+        score: 5
+        max: 5
         passed: true
-        comment: Generic row/column labels, plausible but not real-world scenario
+        comment: 'Weather metrics: real, neutral, scientifically meaningful scenario
+          with realistic relationships'
       - id: DQ-03
         name: Appropriate Scale
-        score: 5
-        max: 5
+        score: 4
+        max: 4
         passed: true
-        comment: Values 8-110 are sensible for demonstration
+        comment: Correlation values -0.77 to 1.00, all plausible for weather data
     code_quality:
       score: 10
       max: 10
@@ -169,40 +191,47 @@ review:
         score: 3
         max: 3
         passed: true
-        comment: Clean imports → data → plot → save structure, no functions/classes
+        comment: Clean imports, data, plot, save structure; no functions/classes
       - id: CQ-02
         name: Reproducibility
-        score: 3
-        max: 3
+        score: 2
+        max: 2
         passed: true
-        comment: np.random.seed(42) set
+        comment: np.random.seed(42)
       - id: CQ-03
         name: Clean Imports
         score: 2
         max: 2
         passed: true
-        comment: All imports are used
+        comment: All imports used (altair, numpy, pandas)
       - id: CQ-04
-        name: No Deprecated API
-        score: 1
-        max: 1
+        name: Code Elegance
+        score: 2
+        max: 2
         passed: true
-        comment: Current Altair API used
+        comment: Clean list comprehension, layered composition, appropriate complexity
       - id: CQ-05
-        name: Output Correct
+        name: Output & API
         score: 1
         max: 1
         passed: true
-        comment: Saves as plot.png and plot.html
-    library_features:
-      score: 3
-      max: 5
+        comment: Saves plot.png and plot.html, no deprecated functions
+    library_mastery:
+      score: 9
+      max: 10
       items:
-      - id: LF-01
-        name: Uses distinctive library features
-        score: 3
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 5
+        max: 5
+        passed: true
+        comment: 'Expert Altair: declarative layers, conditional encoding, transform_filter,
+          proper type encodings'
+      - id: LM-02
+        name: Distinctive Features
+        score: 4
         max: 5
         passed: true
-        comment: Uses alt.condition for text color, tooltip encoding, scale with domainMid,
-          but could use more interactive features
+        comment: alt.when().then().otherwise() conditional encoding, transform_filter,
+          tooltip + HTML export, layer composition
   verdict: APPROVED